The calibration of serial manipulators with high
numbers of degrees of freedom by means of machine learning is
a complex and time-consuming task. With the help of a simple
strategy, this complexity can be drastically reduced and the speed
of the learning procedure can be increased: When the robot is
virtually divided into shorter kinematic chains, these subchains
can be learned separately and, hence, much more efficiently
than the complete kinematics. Such decompositions, however,
require either...

The calibration of serial manipulators with high
numbers of degrees of freedom by means of machine learning is
a complex and time-consuming task. With the help of a simple
strategy, this complexity can be drastically reduced and the speed
of the learning procedure can be increased: When the robot is
virtually divided into shorter kinematic chains, these subchains
can be learned separately and, hence, much more efficiently
than the complete kinematics. Such decompositions, however,
require either the possibility to capture the poses of all endeffectors
of all subchains at the same time, or they are limited to
robots that fulfill special constraints. In this work, an alternative
decomposition is presented that does not suffer from these
limitations. An offline training algorithm is provided in which
the composite subchains are learned sequentially with dedicated
movements. A second training scheme is provided to train
composite chains simultaneously and online. Both schemes can
be used together with many machine learning algorithms. In the
simulations, an algorithm using Parameterized Self-Organizing
Maps (PSOM) modified for online learning and Gaussian Mixture
Models (GMM) were chosen to show the correctness of the
approach. The experimental results show that, using a two-fold
decomposition, the number of samples required to reach a given
precision